These instances, whenmapped to an N-dimensional space, represent a core set that can be used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning problem on these core sets can produce a good approximation solution in very fast speed. For example, the core-vector machine [81] thus produced can learn an SVM for millions of data in seconds.
標(biāo)簽： N-dimensional whenmapped instances represent
上傳時(shí)間： 2016-11-23
上傳用戶：lixinxiang
The combinatorial core of the OVSF code assignment problem that arises in UMTS is to assign some nodes of a complete binary tree of height h (the code tree) to n simultaneous connections, such that no two assigned nodes (codes) are on the same root-to-leaf path. Each connection requires a code on a specified level. The code can change over time as long as it is still on the same level. We consider the one-step code assignment problem: Given an assignment, move the minimum number of codes to serve a new request. Minn and Siu proposed the so-called DCAalgorithm to solve the problem optimally. We show that DCA does not always return an optimal solution, and that the problem is NP-hard. We give an exact nO(h)-time algorithm, and a polynomial time greedy algorithm that achieves approximation ratio Θ(h). Finally, we consider the online code assignment problem for which we derive several results
標(biāo)簽： combinatorial assignment problem arises
上傳時(shí)間： 2014-01-19
上傳用戶：BIBI
The Hilbert Transform is an important component in communication systems, e.g. for single sideband modulation/demodulation, amplitude and phase detection, etc. It can be formulated as filtering operation which makes it possible to approximate the Hilbert Transform with a digital filter. Due to the non-causal and infinite impulse response of that filter, it is not that easy to get a good approximation with low hardware resource usage. Therefore, different filters with different complexities have been implemented. The detailed discussion can be found in "Digital Hilbert Transformers or FPGA-based Phase-Locked Loops" (http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4629940). The design is fully pipelined for maximum throughput.
標(biāo)簽： e.g. communication Transform important
上傳時(shí)間： 2017-06-25
上傳用戶：gxf2016
ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead and Lowe [1], which are motivated by observation on the local response in biologic neurons. Due to their better approximation capabilities, simpler network structures and faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
標(biāo)簽： introduced literature Broomhead Function
上傳時(shí)間： 2017-08-08
上傳用戶：lingzhichao
Abstract—Stable direct and indirect decentralized adaptive radial basis neural network controllers are presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of radial basis neural networks, the dynamics for each subsystem are not required to be linear in a set of unknown coeffi cients as is typically required in decentralized adaptive schemes. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds.
標(biāo)簽： decentralized controllers Abstract adaptive
上傳時(shí)間： 2017-08-17
上傳用戶：gdgzhym
DDSCAT 7.3 is a freely available open-source Fortran-90 software package applying the “discrete dipole approximation” (DDA) to calculate scattering and absorption of electromagnetic waves by targets with arbitrary geometries and complex refractive index. The targets may be isolated entities (e.g., dust particles), but may also be 1-d or 2-d periodic arrays of “target unit cells”, which can be used to study absorption, scattering, and electric ?elds around arrays of nanostructures.
標(biāo)簽： userguide
上傳時(shí)間： 2015-04-29
上傳用戶：499689361
This paper presents a Hidden Markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden Markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.
標(biāo)簽： Telecommunications Processing Signal for
上傳時(shí)間： 2020-06-01
上傳用戶：shancjb
Recent years have seen a rapid development of neural network control tech- niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings.
標(biāo)簽： Stable_adaptive_neural_network_co ntrol
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
蟲蟲下載站版權(quán)所有 京ICP備2021023401號(hào)-1
